Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena

no code implementations • 11 Jun 2022 • Timo Freiesleben, Gunnar König, Christoph Molnar, Alvaro Tejero-Cantero

These descriptors are IML methods that provide insight not just into the model, but also into the properties of the phenomenon the model is designed to represent.

BIG-bench Machine Learning Interpretable Machine Learning

SBI -- A toolkit for simulation-based inference

no code implementations • 17 Jul 2020 • Alvaro Tejero-Cantero, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann, Conor Durkan, Pedro J. Gonçalves, David S. Greenberg, Jakob H. Macke

$\texttt{sbi}$ facilitates inference on black-box simulators for practising scientists and engineers by providing a unified interface to state-of-the-art algorithms together with documentation and tutorials.

Bayesian Inference